slop-grader
Flag lines and grade documents against custom writing rules
Implementation and evidence →JEV GUIDE
“Make this more engaging” gives an author little to act on. slop-grader evaluates explicit rules and reports lines or document dimensions that deserve attention. The author or another writing tool then revises the text. This is not evidence of who wrote a document.
Start with the document’s job. A setup guide needs prerequisites and executable steps; an opinion piece may rely on rhetoric and personal voice. Translate vague preferences into observable questions, such as whether a requested action identifies a target or a numerical claim has support.
Prefer deterministic checks for broken links, required headings and code syntax. Reserve semantic evaluation for requirements that need interpretation.
The reviewed grader constructs Noul questions for line rules and Score questions for document rules. A line finding can locate a specific problem; a document score can help assess structure, prerequisites or task coverage.
A report is input to revision, not a revised manuscript. If a sentence lacks a fact, the author must supply it. A generation tool can propose wording but should not invent the missing evidence.
Source: slop-grader — grader.ts ↗
This editorial Markdown fixture follows the project’s documented format. It has not been executed through the syntax checker or a live model. The rule targets an actionable documentation problem rather than inferring AI authorship.
# Line Rules
## unexplained_action
Does the line ask the reader to change a setting without identifying what to change?
### Criteria
- **true**: A setting change is requested, but no setting or value is identified.
- **false**: The target is specified, or the line does not request a setting change.
# Document Rules
## prerequisites
How clearly does this setup guide state what the reader needs before starting?
### Criteria
- Required inputs and access are absent
- Some requirements are stated, but important ones are missing
- Required inputs and access are identifiable
- Requirements and a way to verify them are explicitSource: slop-grader — README.md ↗
These synthetic cases contain no invented model scores. Have an editor label them first, then investigate whether disagreements come from missing context or unclear criteria.
| Synthetic passage | Review target | Editorial response |
|---|---|---|
| “Optimize the relevant settings.” | No setting is identified | Ask for the field and value |
| “Set timeoutMs to 0 for one evaluation.” | Specific target and value | Do not flag mere brevity |
| “This increases efficiency by 90%.” | Unsupported measurement | Request a source and conditions |
| A prohibited phrase inside a quotation | Quoted context differs from narration | Review rather than automatically replace |
Validate rule syntax before grading a permitted draft. The project’s --check runs without model inference: it does not establish the rule’s accuracy. Keep the draft, rules, model identifier and report, then review false positives before applying edits.
Recheck the original findings and affected passages after revision. Compare names, numbers, citations and conclusions with the original. Do not remove necessary limitations to improve a score. Real grading sends text to the selected provider; the author should control that input scope.
Source: slop-grader — README.md ↗
Can it remove all AI-sounding prose? It checks supplied style criteria, not authorship, and cannot guarantee every reader’s preference.
Does this establish Chinese-language quality? No. Custom rules are possible, but bundled English or German grammar rules do not validate Chinese performance. Build and label language-specific examples.
Should every finding block CI? Begin with advisory reports and review false positives before promoting stable, explainable checks to gates. We reviewed source code and did not ask Jev to grade a manuscript.
Flag lines and grade documents against custom writing rules
Implementation and evidence →03 / JEV GUIDE
Write focused Jev questions with original feedback-routing examples, explicit criteria and a practical edge-case checklist.
Read guide →14 / JEV GUIDE
Compare Jev and generative LLM task boundaries through rewriting and script-review cases. Typed output does not establish correctness or performance.
Read guide →15 / JEV GUIDE
Use semantic-assert to check the meaning of UI feedback with Jev. Separate exact assertions, state capture, retries, redaction and failure evidence.
Read guide →